{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c3a5e62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch    : 1.12.1\n",
      "lightning: 2022.9.15\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -p torch,lightning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "292759a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightning as L\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import torchmetrics\n",
    "from lightning.pytorch.loggers import CSVLogger\n",
    "\n",
    "from shared_utilities import CustomDataModule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ef478510-97a2-497c-8831-bb594b753d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_epochs = 200"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbcee9b9-40cf-4083-b36e-b09fe28e4ee2",
   "metadata": {},
   "source": [
    "### With Restarts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f9bf7772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model = torch.nn.Linear(1, 1)\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n",
    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs/10)\n",
    "lrs = []\n",
    "\n",
    "\n",
    "for i in range(num_epochs):\n",
    "    optimizer.step()\n",
    "    lrs.append(optimizer.param_groups[0][\"lr\"])\n",
    "    scheduler.step()\n",
    "\n",
    "plt.ylabel(\"Learning rate\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.plot(lrs);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce656fef-698a-4d9e-8a2e-0c4e8b67d01b",
   "metadata": {},
   "source": [
    "### Classic cosine annealing (decay per epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0271fa05-1191-4dfa-8f57-c1e9310112ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1576aaca0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "model = torch.nn.Linear(1, 1)\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n",
    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs)\n",
    "lrs = []\n",
    "\n",
    "\n",
    "\n",
    "for i in range(num_epochs):\n",
    "    optimizer.step()\n",
    "    lrs.append(optimizer.param_groups[0][\"lr\"])\n",
    "    scheduler.step()\n",
    "\n",
    "plt.plot(lrs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff5603b4-e089-4489-a51c-1cb41cdf9d27",
   "metadata": {},
   "source": [
    "### Classic cosine annealing (decay per batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6d9c70b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model = torch.nn.Linear(1, 1)\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n",
    "\n",
    "\n",
    "dm = CustomDataModule()\n",
    "dm.setup(\"training\")\n",
    "num_steps = num_epochs * len(dm.train_dataloader())\n",
    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_steps)\n",
    "lrs = []\n",
    "\n",
    "\n",
    "for i in range(num_steps):\n",
    "    optimizer.step()\n",
    "    lrs.append(optimizer.param_groups[0][\"lr\"])\n",
    "    scheduler.step()\n",
    "\n",
    "\n",
    "ax1 = plt.subplot(1, 1, 1)\n",
    "ax1.set_ylabel(\"Learning rate\")\n",
    "ax1.set_xlabel(\"Iterations\")\n",
    "ax1.plot(lrs)\n",
    "\n",
    "###################\n",
    "# Set second x-axis\n",
    "ax2 = ax1.twiny()\n",
    "newlabel = list(range(num_epochs+1))\n",
    "\n",
    "newpos = [e * (num_steps // num_epochs) for e in newlabel]\n",
    "\n",
    "ax2.set_xticks(newpos[::20])\n",
    "ax2.set_xticklabels(newlabel[::20])\n",
    "\n",
    "ax2.xaxis.set_ticks_position('bottom')\n",
    "ax2.xaxis.set_label_position('bottom')\n",
    "ax2.spines['bottom'].set_position(('outward', 45))\n",
    "ax2.set_xlabel('Epochs')\n",
    "ax2.set_xlim(ax1.get_xlim());\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47019085-5f27-4f86-ad1b-18078ee1452c",
   "metadata": {},
   "source": [
    "### Classic cosine annealing (decay per batch) with warmup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9d60d500-df5a-4383-8195-35da730e4450",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code from https://github.com/katsura-jp/pytorch-cosine-annealing-with-warmup/blob/master/cosine_annealing_warmup/scheduler.py\n",
    "\n",
    "from torch.optim.lr_scheduler import _LRScheduler\n",
    "\n",
    "class CosineAnnealingWarmupRestarts(_LRScheduler):\n",
    "    \"\"\"\n",
    "        optimizer (Optimizer): Wrapped optimizer.\n",
    "        first_cycle_steps (int): First cycle step size.\n",
    "        cycle_mult(float): Cycle steps magnification. Default: -1.\n",
    "        max_lr(float): First cycle's max learning rate. Default: 0.1.\n",
    "        min_lr(float): Min learning rate. Default: 0.001.\n",
    "        warmup_steps(int): Linear warmup step size. Default: 0.\n",
    "        gamma(float): Decrease rate of max learning rate by cycle. Default: 1.\n",
    "        last_epoch (int): The index of last epoch. Default: -1.\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self,\n",
    "                 optimizer : torch.optim.Optimizer,\n",
    "                 first_cycle_steps : int,\n",
    "                 cycle_mult : float = 1.,\n",
    "                 max_lr : float = 0.1,\n",
    "                 min_lr : float = 0.001,\n",
    "                 warmup_steps : int = 0,\n",
    "                 gamma : float = 1.,\n",
    "                 last_epoch : int = -1\n",
    "        ):\n",
    "        assert warmup_steps < first_cycle_steps\n",
    "        \n",
    "        self.first_cycle_steps = first_cycle_steps # first cycle step size\n",
    "        self.cycle_mult = cycle_mult # cycle steps magnification\n",
    "        self.base_max_lr = max_lr # first max learning rate\n",
    "        self.max_lr = max_lr # max learning rate in the current cycle\n",
    "        self.min_lr = min_lr # min learning rate\n",
    "        self.warmup_steps = warmup_steps # warmup step size\n",
    "        self.gamma = gamma # decrease rate of max learning rate by cycle\n",
    "        \n",
    "        self.cur_cycle_steps = first_cycle_steps # first cycle step size\n",
    "        self.cycle = 0 # cycle count\n",
    "        self.step_in_cycle = last_epoch # step size of the current cycle\n",
    "        \n",
    "        super(CosineAnnealingWarmupRestarts, self).__init__(optimizer, last_epoch)\n",
    "        \n",
    "        # set learning rate min_lr\n",
    "        self.init_lr()\n",
    "    \n",
    "    def init_lr(self):\n",
    "        self.base_lrs = []\n",
    "        for param_group in self.optimizer.param_groups:\n",
    "            param_group['lr'] = self.min_lr\n",
    "            self.base_lrs.append(self.min_lr)\n",
    "    \n",
    "    def get_lr(self):\n",
    "        if self.step_in_cycle == -1:\n",
    "            return self.base_lrs\n",
    "        elif self.step_in_cycle < self.warmup_steps:\n",
    "            return [(self.max_lr - base_lr)*self.step_in_cycle / self.warmup_steps + base_lr for base_lr in self.base_lrs]\n",
    "        else:\n",
    "            return [base_lr + (self.max_lr - base_lr) \\\n",
    "                    * (1 + math.cos(math.pi * (self.step_in_cycle-self.warmup_steps) \\\n",
    "                                    / (self.cur_cycle_steps - self.warmup_steps))) / 2\n",
    "                    for base_lr in self.base_lrs]\n",
    "\n",
    "    def step(self, epoch=None):\n",
    "        if epoch is None:\n",
    "            epoch = self.last_epoch + 1\n",
    "            self.step_in_cycle = self.step_in_cycle + 1\n",
    "            if self.step_in_cycle >= self.cur_cycle_steps:\n",
    "                self.cycle += 1\n",
    "                self.step_in_cycle = self.step_in_cycle - self.cur_cycle_steps\n",
    "                self.cur_cycle_steps = int((self.cur_cycle_steps - self.warmup_steps) * self.cycle_mult) + self.warmup_steps\n",
    "        else:\n",
    "            if epoch >= self.first_cycle_steps:\n",
    "                if self.cycle_mult == 1.:\n",
    "                    self.step_in_cycle = epoch % self.first_cycle_steps\n",
    "                    self.cycle = epoch // self.first_cycle_steps\n",
    "                else:\n",
    "                    n = int(math.log((epoch / self.first_cycle_steps * (self.cycle_mult - 1) + 1), self.cycle_mult))\n",
    "                    self.cycle = n\n",
    "                    self.step_in_cycle = epoch - int(self.first_cycle_steps * (self.cycle_mult ** n - 1) / (self.cycle_mult - 1))\n",
    "                    self.cur_cycle_steps = self.first_cycle_steps * self.cycle_mult ** (n)\n",
    "            else:\n",
    "                self.cur_cycle_steps = self.first_cycle_steps\n",
    "                self.step_in_cycle = epoch\n",
    "                \n",
    "        self.max_lr = self.base_max_lr * (self.gamma**self.cycle)\n",
    "        self.last_epoch = math.floor(epoch)\n",
    "        for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):\n",
    "            param_group['lr'] = lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c42f404e-1f70-402e-997a-0a810a85ddaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Hcpo8EZGhMACZqOLbXzZaFVRKXkZTVMNWi40vt0V3fzfkFugQui4K3x27JndZRERmgd+cJiotu2h7Bfb+mDZLjRLLhrXAc0FeKNQJvPHDSSzbe0nusoiIqj0GIBPFAdDVh0qpwMfPNsErnWsDAOb/dhYfbo/jbvJERFWIAchEMQBVL5IkYWZvf7zVp2itoOX7LmP69ydRUMjd5ImIqgIDkInKYACqll7uVAefPtcUSoWEzcev45VvonA/jwsmEhFVNgYgE8VtMKqvZ4O88OWwIGhVCuw6m4IRq44gIydf7rKIiKoVBiATVXwLzIEboVZL3QPcsG5Ma9haqHD0yl0MXXEEd+/lyV0WEVG1wQBkoooDkB17gKqtlr5O2PhyGzhZa3AqMR3PLz+MlMwcucsiIqoWGIBMFAdBm4dGnvb47pU2cLXV4tzNTDz/5WEkpd+XuywiIpPHAGSi0hiAzEZdV1t8H9oWNR0scTn1Hp5bFoGE29lyl0VEZNIYgExUBscAmRUfZ2t8F9oWvs5WuH73Pp778hAupmTJXRYRkcmSPQAtXboUfn5+sLCwQFBQEPbv3//Q9nv37kVQUBAsLCxQu3ZtLFu2rFSbhQsXokGDBrC0tIS3tzemTp2KnJzqNXaCt8DMT00HS3z3SlvUd7PBzYxchHwZgbikDLnLIiIySbIGoE2bNmHKlCmYNWsWoqOj0bFjR/Tu3RsJCQllto+Pj0efPn3QsWNHREdH46233sKkSZOwefNmfZv169djxowZmDNnDuLi4rBy5Ups2rQJM2fONNRpGQQDkHlytbPAxpfbIrCmHW7fy8Pzyw/j5PU0ucsiIjI5sgagBQsW4KWXXsKYMWPg7++PhQsXwtvbG2FhYWW2X7ZsGWrVqoWFCxfC398fY8aMwejRo/Hpp5/q20RERKB9+/Z44YUX4Ovrix49emDIkCE4duyYoU6ryuUV6JD91+J4DEDmx8lag/Vj2qBFLQek38/HsK+OMAQREVWQbAEoLy8PUVFR6NGjR4njPXr0wKFDh8p8TURERKn2PXv2xLFjx5CfX9Qj0qFDB0RFRSEyMhIAcPnyZWzfvh19+/Z9YC25ubnIyMjQPzIzMx/n1Kpcce+PJAG2FgxA5sjeUo21L7VGsI8jMnIKGIKIiCpItgCUmpqKwsJCuLm5lTju5uaG5OTkMl+TnJxcZvuCggKkpqYCAJ5//nm8//776NChA9RqNerUqYOuXbtixowZD6xl3rx5sLe31z8CAgIe8+yqVnEAstWqoFRIMldDcrHRqrBmdCt9CBrKEEREVG6yD4KWpJJf4EKIUsf+q/0/j+/ZswcffPABli5diuPHj2PLli345Zdf8P777z/wPWfOnIn09HT9IzY29lFPxyDS7xetCGzPGWBmrzgEtfR1ROZfIejEtTS5yyIiMnqyBSAXFxcolcpSvT0pKSmlenmKubu7l9lepVLB2dkZAPD2229j+PDhGDNmDBo3boyBAwfiww8/xLx586DTlb2ztlarhZ2dnf5ha2tbCWdYdTgAmv7JRqvC6hf/DkHDVjIEERH9F9kCkEajQVBQEMLDw0scDw8PR7t27cp8Tdu2bUu137FjB4KDg6FWF4WB7OxsKBQlT0upVEIIoe8tMnX6fcAsNTJXQsairBAUwxBERPRAst4CmzZtGr766iusWrUKcXFxmDp1KhISEhAaGgqg6NbUiBEj9O1DQ0Nx9epVTJs2DXFxcVi1ahVWrlyJ6dOn69v069cPYWFh2LhxI+Lj4xEeHo63334b/fv3h1KpNPg5VoV07gRPZbDRqrDmHyFoOEMQEdEDqeT88JCQENy+fRtz585FUlISAgMDsX37dvj4+AAAkpKSSqwJ5Ofnh+3bt2Pq1KlYsmQJPD09sWjRIjzzzDP6NrNnz4YkSZg9ezYSExNRo0YN9OvXDx988IHBz6+qpHEjVHoA679C0KjVkTh65S6GrzyCDWPbILCmvdylEREZFUlUl/tClej69evw9vbGtWvX4OXlJXc5pbz38xmsPngF47rUwZu9GspdDhmhe7kF+hDkaKXGxpfbooG7cY9tIyJ6XBX5/pZ9FhhVHAdB03+x1qqwalRLNPWyx93sfAz96jAu3eLeYURExRiATFAGAxCVg62FGl+PbgV/DzukZuVh6Ioj3EWeiOgvDEAmKI2DoKmcHKw0WPdSK9RztUFyRg6GrDiMG2n35S6LiEh2DEAm6O9p8AxA9N+cbbRYP6Y1fJ2tkJh2Hy+sOIyUjBy5yyIikhUDkAlK5ywwqiBXOwt8O7YNajpY4srtbAz96ghuZ+XKXRYRkWwYgEwQB0HTo/B0sMSGsW3gbmeBCylZGLYyEmnZeXKXRUQkCwYgE5OTX4jcgqItPbgXGFVULWcrrB/bGi42WsQlZWDkqkhk5RbIXRYRkcExAJmY4t4fpUKCrVbWdSzJRNWpYYP1Y1rDwUqNE9fT8fLaY8jJL5S7LCIig2IAMjH68T8WKkiSJHM1ZKoauNvi6xdbwVqjxKFLtzFpQzQKCsveLJiIqDpiADIxHP9DlaWptwNWjAyGRqXAjtibmLHlFHQ6LgxPROaBAcjEcA0gqkzt6rhg8ZDmUCok/BB1Hf/3axy4Ow4RmQMGIBOj7wGy0shcCVUXPRq546NnmgAAVh2Mx+I/L8pcERFR1WMAMjG8BUZV4dkgL7zzVAAA4LPw8/gm4oq8BRERVTEGIBPzdwDiDDCqXKM7+GFSt3oAgHe2ncFPMYkyV0REVHUYgExM+l8L17EHiKrC1O71MLKtD4QAXvvuBP48e1PukoiIqgQDkIn5ex8wjgGiyidJEub0a4QBzTxRoBMYt+44IuPvyF0WEVGlYwAyMRwDRFVNoZDwyXNN0a2hK3ILdBjz9VGcv5kpd1lERJWKAcjEpHEjVDIAtVKBxS+0QJCPIzJyCjByVSRupN2XuywiokrDAGRi2ANEhmKpUWLlyGDUdbVBUnoORq7i5qlEVH0wAJmYjOIxQNwIlQzAwUqDr0e3gpudFhdSsjDma+4bRkTVAwOQCRFCsAeIDK6mgyW+Ht0KthYqHLt6F69y3zAiqgYYgExIdl4h8guLtilgACJDauhuh69GFO0bFh57E2//dIZbZhCRSWMAMiHFvT9qpQQrjVLmasjctK7tjEXPN4MkARsiE7BoF7fMICLTxQBkQv55+0uSJJmrIXPUK9ADc58OBAB8vvM8NkQmyFwREdGjYQAyIemcAk9GYHgbH7z6RF0AwKytpxAey9Wiicj0MACZkLRsDoAm4zDtyfoICfaGTgATvz2OqKtcLZqITAsDkAnRT4FnACKZSZKEDwYG/mO16GOIT70nd1lEROXGAGRCOAWejInqr9Wim3rZ4252PkatjsTtrFy5yyIiKhcGIBPCAETGxlKjxFcjW8LbyRJXb2djzFoulEhEpoEByISk3S/ahoABiIxJDVstVo9qBXtLNaIT0jBlYwwKdVwjiIiMGwOQCUm/XwAAsLfSyFwJUUl1XW2wYkQwNEoFfj+TjA+3x8ldEhHRQzEAmRDeAiNj1srPCZ8ObgoAWHkgHmsOxstcERHRgzEAmRAGIDJ2/Zt64o1eDQAA7/0Six1nkmWuiIiobAxAJiQ9m2OAyPiN61wHL7SuBSGASRujEXMtTe6SiIhKYQAyIcU9QA5WDEBkvCRJwtz+jdC1QQ3k5Ovw0pqjSLidLXdZREQlMACZCJ1O8BYYmYziNYIaedrh9r08jFoTibv38uQui4hIjwHIRGTlFaB4ZjEDEJkCa60Kq0a1RE0HS1y+dQ8vf8M1gojIeDAAmYj0v/YB06gUsFArZa6GqHzc7Cyw+sWWsLVQ4eiVu3jjh5MQgmsEEZH8GIBMRDr3ASMTVd/NFl8OC4JKIWHbiRtYuPOC3CURETEAmYoMjv8hE9aurgv+b0AgAOCLXRfwU0yizBURkbljADIRaQxAZOKeb1ULr3SqDQB4/fuTOHbljswVEZE5YwAyEZwCT9XBm70aokeAG/IKdXj5myhOjyci2TAAmYjiAGTHHiAyYQqFhIXPN0NgTTvcuZeH0V8f1f/dJiIyJAYgE8E1gKi6sNKosHJkS7jbWeBiShYmrD+O/EKd3GURkZlhADIRadkMQFR9uNlZ4KuRwbBUK3HgYirmbDvD6fFEZFAMQCYig9PgqZoJrGmPRUOaQ5KAb48kYOUB7h5PRIbDAGQi9LfAOAiaqpEnA9wwq48/AOCD7XEIj70pc0VEZC4YgEwExwBRdfVSBz/97vGTN0bjdGK63CURkRl4pABUUFCAnTt34ssvv0RmZiYA4MaNG8jKyqrU4uhvafeLNpJkAKLqRpIkvNe/ETrWc0F2XiHGfH0Myek5cpdFRNVchQPQ1atX0bhxYzz99NOYMGECbt26BQD4+OOPMX369EovkIqk6wdBa2SuhKjyqf/aPb6uqw2SM3IwZu1RZOcVyF0WEVVjFQ5AkydPRnBwMO7evQtLS0v98YEDB2LXrl2VWhwV0ekEMnOLvgzYA0TVlb2lGqtGtoSTtQanEzMwdVMMdDrODCOiqlHhAHTgwAHMnj0bGk3JnggfHx8kJnJ/n6qQmVOA4hnCDEBUndVytsLy4UHQKBX448xNfL7zvNwlEVE1VeEApNPpUFhYWOr49evXYWtrWylFUUnF438s1UpoVBy3TtVbsK8TPhzUGADwvz8vcuNUIqoSFf42ffLJJ7Fw4UL9nyVJQlZWFubMmYM+ffpUZm30F+4DRubm2SAv/capb/xwEjHX0uQtiIiqnQoHoM8//xx79+5FQEAAcnJy8MILL8DX1xeJiYn46KOPqqJGs8cp8GSO3ujVEN0auiK3QIeX13JmGBFVrgoHIE9PT8TExOD111/HK6+8gubNm2P+/PmIjo6Gq6trVdRo9oq3weBGqGROlH9tnFrfzQYpmbkYu/YY7ueVvv1ORPQoKhyA9u3bB7VajRdffBGLFy/G0qVLMWbMGKjVauzbt68qajR77AEic2VrocZXI1rC0UqNU4npmP7DCe4ZRkSVosIBqGvXrrhz506p4+np6ejatWuFC1i6dCn8/PxgYWGBoKAg7N+//6Ht9+7di6CgIFhYWKB27dpYtmxZqTZpaWmYMGECPDw8YGFhAX9/f2zfvr3CtRmLdO4DRmaslrMVlg0Lgkoh4deTSVi066LcJRFRNVDhACSEgCRJpY7fvn0b1tbWFXqvTZs2YcqUKZg1axaio6PRsWNH9O7dGwkJCWW2j4+PR58+fdCxY0dER0fjrbfewqRJk7B582Z9m7y8PDz55JO4cuUKfvjhB5w7dw4rVqxAzZo1K3aiRiSDPUBk5lrXdsb/DQgEAHy+8zx+PZkkc0VEZOpU5W04aNAgAEWzvkaNGgWtVqt/rrCwECdPnkS7du0q9OELFizASy+9hDFjxgAAFi5ciD/++ANhYWGYN29eqfbLli1DrVq19LPQ/P39cezYMXz66ad45plnAACrVq3CnTt3cOjQIajVRYHBx8enQnUZm7RsBiCi51vVwvmbWVh1MB6vfR8DH2crBNa0l7ssIjJR5e4Bsre3h729PYQQsLW11f/Z3t4e7u7uePnll7Fu3bpyf3BeXh6ioqLQo0ePEsd79OiBQ4cOlfmaiIiIUu179uyJY8eOIT+/KCRs27YNbdu2xYQJE+Dm5obAwEB8+OGHZa5dVCw3NxcZGRn6R/H+ZsaC0+CJirzVpyE61a+BnHwdxnx9DCkZnBlGRI+m3D1Aq1evBgD4+vpi+vTpFb7d9W+pqakoLCyEm5tbieNubm5ITk4u8zXJycllti8oKEBqaio8PDxw+fJl/Pnnnxg6dCi2b9+OCxcuYMKECSgoKMA777xT5vvOmzcP77333mOdT1UqDkCcBUbmTqVUYPELzTFwyUFcunUPY7+JwqaX28BCrZS7NCIyMRUeAzRnzpzHDj//9O/xRA8aY/Sw9v88rtPp4OrqiuXLlyMoKAjPP/88Zs2ahbCwsAe+58yZM5Genq5/xMbGPurpVAnOAiP6m52FGitHtoS9pRonrqXhzc0nOTOMiCqs3D1A//TDDz/gu+++Q0JCAvLy8ko8d/z48XK9h4uLC5RKZanenpSUlFK9PMXc3d3LbK9SqeDs7AwA8PDwgFqthlL5978I/f39kZycjLy8vFJ7mAGAVqstMaYpIyOjXOdgKAxARCX5ulgjbGgLjFgViZ9ibqC+my0mdK0rd1lEZEIq3AO0aNEivPjii3B1dUV0dDRatWoFZ2dnXL58Gb179y73+2g0GgQFBSE8PLzE8fDw8AcOpm7btm2p9jt27EBwcLB+wHP79u1x8eJF6HQ6fZvz58/Dw8OjzPBjCv4eA2Sa9RNVhXZ1XfBu/0YAgE/+OIc/zpR965yIqCwVDkBLly7F8uXLsXjxYmg0GrzxxhsIDw/HpEmTkJ6eXqH3mjZtGr766iusWrUKcXFxmDp1KhISEhAaGgqg6NbUiBEj9O1DQ0Nx9epVTJs2DXFxcVi1ahVWrlyJ6dOn69uMGzcOt2/fxuTJk3H+/Hn8+uuv+PDDDzFhwoSKnqpRKCjUISu3AAB7gIj+bVgbH4xoWzTLc+qmGMTeMK7eWyIyXhUOQAkJCfoeGktLS/2MqeHDh2PDhg0Veq+QkBAsXLgQc+fORbNmzbBv3z5s375dP209KSmpxJpAfn5+2L59O/bs2YNmzZrh/fffx6JFi/RT4AHA29sbO3bswNGjR9GkSRNMmjQJkydPxowZMyp6qkYhI6dA/992Fo90x5KoWnvnqQB0qOuC7LxCjF17DLezcuUuiYhMQIW/Ud3d3XH79m34+PjAx8cHhw8fRtOmTREfH/9IAxHHjx+P8ePHl/ncmjVrSh3r3Lnzf44zatu2LQ4fPlzhWoxRWnbRGCsbrQoqZYXzKlG1p1IqsOSFFnh6yQFcuZ2N8euPY92Y1lDz94WIHqLC/w/xxBNP4OeffwYAvPTSS5g6dSqefPJJhISEYODAgZVeoLnjAGii/2ZvpcZXI4Nho1XhSPwdvPfzGblLIiIjV+EeoOXLl+sHGIeGhsLJyQkHDhxAv3799GN3qPIwABGVT11XWywMaYax3xzDusMJ8Peww9DWpr0KPBFVnQr1ABUUFOD9999HUtLf+/AMHjwYixYtwqRJk0x2lpUxYwAiKr/uAW6Y3qMBAGDOT2dw5PJtmSsiImNVoQCkUqnwySefPHRbCapcDEBEFTO+Sx081cQDBTqB8euP4/rdbLlLIiIjVOExQN27d8eePXuqoBQqS3o29wEjqghJkvDJs03RyNMOt+/l4eW1UcjOK/jvFxKRWanwGKDevXtj5syZOH36NIKCgkpti9G/f/9KK47YA0T0KCw1SiwfEYynFx9AbFIGXv/+JBa/0Pyh2+wQkXmpcAAaN24cAGDBggWlnpMkibfHKhk3QiV6NDUdLBE2LAgvrDiMX08lwX+3LSY+UU/usojISFT4FphOp3vgg+Gn8qWxB4jokbX0dcLcpwMBAJ/uOI/w2JsyV0RExoIrhRm5v/cBYwAiehRDWtXC8DZF0+GnbIzG+ZuZMldERMaAAcjIZbAHiOixvdMvAG1qO+HeX9tlFK+wTkTmiwHIyKVlMwARPS61UoGlQ4Pg5WiJq7ezMfHbaBQU6uQui4hkxABk5PS3wCy5yCTR43Cy1mDFiGBYaZQ4cDEVH24/K3dJRCQjBiAjllegw/38ooHl7AEienz+Hnb47LmmAIBVB+Px/bFrMldERHKpcADKyMgo85GZmYm8PN5Xr0zFvT+SBNhaVHjFAiIqQ+/GHpjUrWg6/Kytp3E84a7MFRGRHCocgBwcHODo6Fjq4eDgAEtLS/j4+GDOnDn6DVPp0aXfLwqUtloVFAou4EZUWaZ0q4eejdyQV6jDK99EITk9R+6SiMjAKhyA1qxZA09PT7z11lv48ccfsXXrVrz11luoWbMmwsLC8PLLL2PRokWYP39+VdRrVv6eAs/xP0SVSaGQsGBwMzRws8WtzFy88s0x5ORzHTMic1Lh+ypff/01PvvsMwwePFh/rH///mjcuDG+/PJL7Nq1C7Vq1cIHH3yAt956q1KLNTfcBoOo6lhrVVgxIhj9lxzAievpmLnlFBYMbsrtMojMRIV7gCIiItC8efNSx5s3b46IiAgAQIcOHZCQkPD41Zk5BiCiqlXL2QpLX2gBpULC1uhErNh/We6SiMhAKhyAvLy8sHLlylLHV65cCW9vbwDA7du34ejo+PjVmTmuAURU9drVdcE7TwUAAOb/dhZ7zqXIXBERGUKFb4F9+umneO655/Dbb7+hZcuWkCQJR48exdmzZ/HDDz8AAI4ePYqQkJBKL9bc6HuAuA0GUZUa0dYHsTcysOnYNby6IRo/TWiP2jVs5C6LiKpQhXuA+vfvj3PnzqF37964c+cOUlNT0bt3b5w9exZPPfUUgKId48vaLZ4qhrfAiAxDkiTMHdAIQT6OyMwpwJi1x5CRky93WURUhR5pcRlfX1/O8jIABiAiw9GqlFg2LAj9Fx/A5Vv3MHlDNL4a2RJKLkFBVC09UgBKS0tDZGQkUlJSSq33M2LEiEopjIB0jgEiMqgatlosHx6MZ5cdwu5zt/DJH+cwo3dDucsioipQ4QD0888/Y+jQobh37x5sbW1LTBmVJIkBqBL9vQ8YAxCRoTT2ssfHzzbB5I0xWLb3Evw9bPF0s5pyl0VElazCY4Bee+01jB49GpmZmUhLS8Pdu3f1jzt37lRFjWaLt8CI5PF0s5oY16UOAOCNH07i5PU0eQsiokpX4QCUmJiISZMmwcrKqirqoX8oDkB2DEBEBje9RwM80dAVuQVF22WkZHK7DKLqpMIBqGfPnjh27FhV1EL/ksYeICLZKBUSFj7fDHVqWCMpPQfj1h1HbgG3yyCqLio8Bqhv3754/fXXERsbi8aNG0OtLvnl3L9//0orzpzl5Bcir6BogLkD1wEikoWdhRorRgTj6SUHEXX1Lt758QzmP9OY22UQVQMVDkBjx44FAMydO7fUc5IkobCQ/0KqDMW3v5QKCTbaR5qsR0SVoHYNGyx+oQVeXB2JTceuwd/DFqPa+8ldFhE9pgrfAtPpdA98MPxUnuJtMOwsVPzXJpHMOtevgZm9/QEA7/8ah4MXU2WuiIgeV4UDEBmGfgq8lUbmSogIAMZ09MOg5jVRqBOY8O1xJNzOlrskInoM5bq3smjRIrz88suwsLDAokWLHtp20qRJlVKYueMMMCLjIkkSPhzUGJduZeHE9XSMWXsUW8a35y1qIhNVrt/czz//HEOHDoWFhQU+//zzB7aTJIkBqJJwDSAi42OhVuLL4cHov/gAzt/MwtRNMfhyWBAU3C6DyOSUKwDFx8eX+d9UddKy8wAwABEZG3d7CywbHoTnvzyM8NibWLjzPKb1aCB3WURUQRwDZKQyuA0GkdFqUcsRHw5qDABY9OdFbD+VJHNFRFRRFb55XVhYiDVr1mDXrl1lbob6559/Vlpx5oy3wIiM27NBXohLysDKA/F47bsT8HW2RoCnndxlEVE5VTgATZ48GWvWrEHfvn0RGBjIKdpVhAGIyPjN7N0Q529mYv+FVIxdewzbJraHs41W7rKIqBwqHIA2btyI7777Dn369KmKeugv3AaDyPiplAosHtICTy85gCu3szF+/XGsG9MaaiVHFxAZuwr/lmo0GtStW7cqaqF/0PcAcRsMIqNmb1W0XYaNVoUj8Xfw3s9n5C6JiMqhwgHotddewxdffAEhRFXUQ3/hLTAi01HPzRYLQ5pBkoB1hxOw/shVuUsiov9Q4VtgBw4cwO7du/Hbb7+hUaNGpTZD3bJlS6UVZ84yGICITEr3ADdM79EAn/xxDnN+OoO6NWzQuraz3GUR0QNUOAA5ODhg4MCBVVEL/UUIod8LjAGIyHSM71IHcUkZ+OVkEsatP45tE9vDy9FK7rKIqAwVCkAFBQXo0qULevbsCXd396qqyexl5xWiQFd0i9GBY4CITIYkSfjk2aaIT72HMzcyMHZtFDaPawsrDbfLIDI2FRoDpFKpMG7cOOTm5lZVPYS/x/+olRIs1UqZqyGiirDUKLF8RDBcbDSIS8rA69+f5JhJIiNU4UHQrVu3RnR0dFXUQn/55wBorrNEZHpqOlgibFgQ1EoJv55KwpLdF+UuiYj+pcL9suPHj8drr72G69evIygoCNbW1iWeb9KkSaUVZ66Kx/9wJ3gi09XS1wlznw7EzC2n8OmO82jgbocnA9zkLouI/lLhABQSEgIAJXZ9lyQJQghIkoTCwsLKq85MpXMfMKJqYUirWoi9kYFvDl/FlI3R2DqhPeq72cpdFhHhEQIQd4OvepwCT1R9vNMvABdSMnH48h2MXXsMP01oDwcrjdxlEZm9CgcgHx+fqqiD/oGLIBJVH2qlAkuHBqH/4gO4ejsbE7+NxpoXW0LF7TKIZPXIczNjY2ORkJCAvLy8Esf79+//2EWZu7T7RT9T/iuRqHpwstZgxYhgPBN2CAcupuKD7XGY06+R3GURmbUKB6DLly9j4MCBOHXqlH7sDwD9bCWOAXp8xT1AHARNVH34e9hhweCmCF13HKsPXoG/hx0GB3vLXRaR2apwH+zkyZPh5+eHmzdvwsrKCmfOnMG+ffsQHByMPXv2VEGJ5if9fgEA3gIjqm56BXpgcrd6AIDZW08j6updmSsiMl8VDkARERGYO3cuatSoAYVCAYVCgQ4dOmDevHklZobRo0vLLroFxgBEVP1M7lYPPRu5Ia9Qh1e+iUJS+n25SyIySxUOQIWFhbCxsQEAuLi44MaNGwCKBkefO3eucqszUxmcBk9UbSkUEhYMboYGbrZIzcrFy2ujkJPPoQNEhlbhABQYGIiTJ08CKFoV+uOPP8bBgwcxd+5c1K5du9ILNEf6WWDcB4yoWrLWqrBiRDAcrNQ4lZiOGZu5XQaRoVU4AM2ePRs6nQ4A8H//93+4evUqOnbsiO3bt2PRokWVXqA54jR4ouqvlrMVlr7QAkqFhB9jbmD5vstyl0RkVio8C6xnz576/65duzZiY2Nx584dODo6ct+qSqDTCQYgIjPRrq4L3nkqAHO2ncH838+inpsNnmjI7TKIDOGRV+K6ePEi/vjjD9y/fx9OTk6PXMDSpUvh5+cHCwsLBAUFYf/+/Q9tv3fvXgQFBcHCwgK1a9fGsmXLHth248aNkCQJAwYMeOT6DC0rrwC6v3rCGYCIqr8RbX0wpJU3hAAmbYjBueRMuUsiMgsVDkC3b99Gt27dUL9+ffTp0wdJSUkAgDFjxuC1116r0Htt2rQJU6ZMwaxZsxAdHY2OHTuid+/eSEhIKLN9fHw8+vTpg44dOyI6OhpvvfUWJk2ahM2bN5dqe/XqVUyfPh0dO3as6CnKKv2vjVC1KgUs1EqZqyGiqiZJEt7rH4jWfk7Iyi3AS18fxe2sXLnLIqr2KhyApk6dCrVajYSEBFhZWemPh4SE4Pfff6/Qey1YsAAvvfQSxowZA39/fyxcuBDe3t4ICwsrs/2yZctQq1YtLFy4EP7+/hgzZgxGjx6NTz/9tES7wsJCDB06FO+9957JDczm7S8i86NRKRA2LAi1nKxw/e59hK6LQm4BZ4YRVaUKB6AdO3bgo48+gpeXV4nj9erVw9WrV8v9Pnl5eYiKikKPHj1KHO/RowcOHTpU5msiIiJKte/ZsyeOHTuG/Px8/bHidYpeeumlctWSm5uLjIwM/SMzU74uaAYgIvPkZK3BypHBsNWqcPTKXczeepozw4iqUIUD0L1790r0/BRLTU2FVqst9/ukpqaisLAQbm4lB/y5ubkhOTm5zNckJyeX2b6goACpqakAgIMHD2LlypVYsWJFuWuZN28e7O3t9Y+AgIByv7ayFQcgB06BJzI79dxs8b8XmkMhAd9HXceK/ZwZRlRVKhyAOnXqhLVr1+r/LEkSdDodPvnkE3Tt2rXCBfx75pgQ4qGzycpqX3w8MzMTw4YNw4oVK+Di4lLuGmbOnIn09HT9IzY2tgJnULnYA0Rk3ro0cMXbTxX9I2zeb2exM/amzBURVU8Vngb/ySefoEuXLjh27Bjy8vLwxhtv4MyZM7hz5w4OHjxY7vdxcXGBUqks1duTkpJSqpenmLu7e5ntVSoVnJ2dcebMGVy5cgX9+vXTP1+8ZpFKpcK5c+dQp06dUu+r1WpL9F5lZGSU+zwqGzdCJaJR7XxxISUL3x5JwOSN0fhhXDv4e9jJXRZRtVLhHqCAgACcPHkSrVq1wpNPPol79+5h0KBBiI6OLjNcPIhGo0FQUBDCw8NLHA8PD0e7du3KfE3btm1Ltd+xYweCg4OhVqvRsGFDnDp1CjExMfpH//790bVrV8TExMDb2/h3Xk7LZg8QkbkrmhnWCO3qOONeXiHGfH0MqZwZRlSpKtwDBBT1xLz33nsljl27dg2jR4/GqlWryv0+06ZNw/DhwxEcHIy2bdti+fLlSEhIQGhoKICiW1OJiYn6W26hoaFYvHgxpk2bhrFjxyIiIgIrV67Ehg0bAAAWFhYIDAws8RkODg4AUOq4sdKPAbLUyFwJEclJrVRg6dAWGLDkIK7czsYr30Th27GtoVVxeQyiyvDICyH+2507d/D1119X6DUhISFYuHAh5s6di2bNmmHfvn3Yvn07fHx8AABJSUkl1gTy8/PD9u3bsWfPHjRr1gzvv/8+Fi1ahGeeeaayTkN2GfoxQI+UTYmoGnGw0mDlqJawtVAh6updzNxyijPDiCqJJCrpt+nEiRNo0aIFCgtNf+2K69evw9vbG9euXSs13b+qDfvqCA5cTMXnIU0xsLlhP5uIjNP+C7cwavVRFOoE3uzVEOO6lH+4AZE5qcj3d6X1AFHlSLufB4BjgIjobx3r1cCcfkUzwz7+4yz+OFP2UiFEVH4MQEbm72nwHANERH8b0dYXw9v4QAhg6qYYnLmRLndJRCat3ANNBg0a9NDn09LSHrcWwt97gbEHiIj+7Z1+AYhPvYcDF1Mx9utj+HFie7jaWshdFpFJKncP0D9XSi7r4ePjgxEjRlRlrdVeoU4gI6cAAAMQEZWmViqw5IUWqO1ijRvpOXh5bRRy8k1/3CWRHMrdA7R69eqqrIMAZOb8vZ8ZAxARlcXeSo2Vo1piwJKDiLmWhjd+OIkvnm/20BX0iag0jgEyIsXjf6w0SmhUvDREVDY/F2uEDW0BlULCthM38Hn4eblLIjI5/JY1ItwHjIjKq11dF3w4sDEAYNGfF7E56rrMFRGZFgYgI8JtMIioIga39NavCTRjy0lEXLotc0VEpoMByIiwB4iIKur1Hg3Qt7EH8gsFQtdF4dKtLLlLIjIJDEBGhAGIiCpKoZDw2eCmaF7LAen38zF6zVHcuZcnd1lERo8ByIgwABHRo7BQK7FiRDC8HC1x9XY2Xl57jNPjif4DA5ARYQAiokflYqPFmheLNk49dvUu3vjhJDdOJXoIBiAjUrwKtIMVAxARVVxdV1ssGxbE6fFE5cAAZETYA0REj6t9XRd8MDAQAKfHEz0MA5ARKQ5AdgxARPQYQlrW4vR4ov/AAGRE0tgDRESVhNPjiR6OAciIZNwvHgOkkbkSIjJ1nB5P9HAMQEaEY4CIqDL9e3r8WE6PJ9JjADIS+YU6ZOUWAGAAIqLKUzw93s5ChairdzF5YzQKdZweT8QAZCSKb38BgJ2FSsZKiKi6qetqi+UjgqFRKvDHmZt4/5dYrhFEZo8ByEgU3/6y1aqgUvKyEFHlalPbGZ8NbgoAWHPoCr7aHy9zRUTy4jetkeAUeCKqav2aemJWH38AwAfb47DtxA2ZKyKSDwOQkeAAaCIyhDEd/fBie18AwPTvTuDwZa4RROaJAchIpN/nNhhEVPUkScLsvgHoHeiOvEIdXl57DOdvZspdFpHBMQAZCfYAEZGhKBUSPg9phmAfR2TkFGDUqkjczMiRuywig2IAMhLFG6EyABGRIRSvEVS7hjVupOdg1OqjyMzJ/+8XElUTDEBGgttgEJGhOVpr8PWLreBio0VcUgbGrTuOvAKd3GURGQQDkJHQ3wLjGCAiMiBvJyusHtUSVholDlxMxYwtJ7lGEJkFBiAjwTFARCSXxl72WDK0BZQKCVuOJ+KTP87JXRJRlWMAMhIMQEQkp64NXDFvYGMAwNI9l7DqABdKpOqNAchIcBA0EcltcEtvvN6zAQBg7i+x+DE6UeaKiKoOA5CR0K8DZKmRuRIiMmfju9TBqHa+AIDp35/AnnMp8hZEVEUYgIwEb4ERkTGQJAnvPBWA/k09UaATGLfuOKIT7spdFlGlYwAyArkFhbifXwiAAYiI5KdQSPj0uaboVL8G7ucX4sU1R3ExhatFU/XCAGQEint/JAmwtVDJXA0REaBRKRA2tAWaejsgLTsfI1ZG4kbafbnLIqo0DEBGIKN4J3gLNRQKSeZqiIiKWGtVWD2qpX616BGrInH3Xp7cZRFVCgYgI8DxP0RkrJysNfjmpdZwt7PAxZQsjP76KLLzCuQui+ixMQAZAQYgIjJmNR0s8c1LrWBvqUZ0QhrGrTuO/EJumUGmjQHICKRxDSAiMnL13GyxalRLWKgV2Hv+FqZ/fwI6HbfMINPFAGQEuA8YEZmCIB9HhA0Lgkoh4aeYG5j902nuG0YmiwHICPAWGBGZiq4NXPF5SDNIEvDtkQTM++0sQxCZJAYgI8AARESmpF9TT8wfVLRv2PJ9l7H4z4syV0RUcQxARqB4HzAHBiAiMhEhLWvh7acCAACfhZ/n5qlkchiAjAB7gIjIFL3UwQ9Tu9cHULR56ndHr8lcEVH5MQAZAQYgIjJVk7rVxdiOfgCAGVtO4peTN2SuiKh8GICMQBoDEBGZKEmS8FYffwxp5Q2dAKZsjMHus9xBnowfA5AR4DR4IjJlkiTh/wY01u8gH7ouChGXbstdFtFDMQAZAd4CIyJTp1RI+GxwU3T3d0VugQ5jvj6KqKt35S6L6IEYgGSWk1+IvIKiJeUZgIjIlKmVCix+oQXa13XGvbxCjFoViZhraXKXRVQmBiCZFW+DoVRIsNGqZK6GiOjxWKiVWDEiGK39nJCZW4ARK4/g1PV0ucsiKoUBSGb/vP0lSZLM1RARPT4rjQqrRrVES19HZOQUYNjKIzhzgyGIjAsDkMw4/oeIqiNrrQqrX2yFFrUckH4/H8O+OoKzyRlyl0WkxwAks+IAZMcARETVjI1WhTWjW6GptwPuZudj6IojuHAzU+6yiAAwAMkuLTsPAHuAiKh6srNQY+3oVmhc0x637+VhyIojuJiSJXdZRAxAcivuAeI+YERUXdlbqvHNS60Q4GGH1KxcvLDiMOJT78ldFpk5BiCZZXAMEBGZAQcrDdaPaY2G7rZIyczFkOUMQSQvBiCZcRA0EZkLR+uiEFTfzQbJGTkI+TICF1M4JojkIXsAWrp0Kfz8/GBhYYGgoCDs37//oe337t2LoKAgWFhYoHbt2li2bFmJ51esWIGOHTvC0dERjo6O6N69OyIjI6vyFB4L9wEjInPibKPFhrFt9D1Bzy8/jHPJDEFkeLIGoE2bNmHKlCmYNWsWoqOj0bFjR/Tu3RsJCQllto+Pj0efPn3QsWNHREdH46233sKkSZOwefNmfZs9e/ZgyJAh2L17NyIiIlCrVi306NEDiYmJhjqtCuE+YERkbopDUCNPO6Rm5eH55RFcJ4gMThJCCLk+vHXr1mjRogXCwsL0x/z9/TFgwADMmzevVPs333wT27ZtQ1xcnP5YaGgoTpw4gYiIiDI/o7CwEI6Ojli8eDFGjBhRrrquX78Ob29vXLt2DV5eXhU8q4oZuPQgohPS8OXwIPRs5F6ln0VEZEzSs/MxYtURnLieDntLNda91BqNvezlLotMWEW+v2XrAcrLy0NUVBR69OhR4niPHj1w6NChMl8TERFRqn3Pnj1x7Ngx5Ofnl/ma7Oxs5Ofnw8nJ6YG15ObmIiMjQ//IzDRcdyzHABGRubK3UuObMa31iyW+8NVhHE/gBqpkGLIFoNTUVBQWFsLNza3EcTc3NyQnJ5f5muTk5DLbFxQUIDU1tczXzJgxAzVr1kT37t0fWMu8efNgb2+vfwQEBFTwbB5d+l97gTnwFhgRmSE7CzXWvtQarXydkJlTgBErI3H0yh25yyIzIPsg6H/vfyWEeOieWGW1L+s4AHz88cfYsGEDtmzZAgsLiwe+58yZM5Genq5/xMbGVuQUHpkQgj1ARGT2ilaMbom2tZ2RlVuAkasicehS2f+oJaossgUgFxcXKJXKUr09KSkppXp5irm7u5fZXqVSwdnZucTxTz/9FB9++CF27NiBJk2aPLQWrVYLOzs7/cPW1vYRzqjisvMKUaArCnAMQERkzoo3UO1YzwXZeYV4cfVR7Iq7KXdZVI3JFoA0Gg2CgoIQHh5e4nh4eDjatWtX5mvatm1bqv2OHTsQHBwMtfrvAPHJJ5/g/fffx++//47g4ODKL76SFE+BVyslWKqVMldDRCQvS40SK0YEo7u/G3ILdHjlmyj8FGOcM3jJ9Ml6C2zatGn46quvsGrVKsTFxWHq1KlISEhAaGgogKJbU/+cuRUaGoqrV69i2rRpiIuLw6pVq7By5UpMnz5d3+bjjz/G7NmzsWrVKvj6+iI5ORnJycnIyjK+vWeKx//YW2oeetuPiMhcWKiVCBvWAgOb10SBTmDKphh8c/iq3GVRNaSS88NDQkJw+/ZtzJ07F0lJSQgMDMT27dvh4+MDAEhKSiqxJpCfnx+2b9+OqVOnYsmSJfD09MSiRYvwzDPP6NssXboUeXl5ePbZZ0t81pw5c/Duu+8a5LzK6+/xP7JeBiIio6JWKvDZc01ha6HC2oirePvH08i4n4/xXerwH4tUaWRdB8hYGWodoN9PJyN0XRRa1HLAlvHtq+xziIhMkRACC8LP439/XgQAvNKpNmb0bsgQRA9kEusAEZB+Pw8AB0ATEZVFkiS81qMBZvf1BwB8ue8y3tp6CoU6/rudHh8DkIyKb4E5WGlkroSIyHiN6VgbHz/TBAoJ2BB5DZM2RCO3oFDussjEMQDJiGsAERGVz+CW3lj8QguolRJ+PZWEkasi9f8fSvQoGIBkVPzLa8cARET0n/o09sDqUa1go1Xh8OU7CPkyAsnpOXKXRSaKAUhGadnsASIiqogO9Vyw6ZU2qGGrxdnkTAxaehDnbxpu/0aqPhiAZKQfA8QARERUbo087bFlXDvUrmGNG+k5eDbsECLjuX8YVQwDkIwyOAaIiOiReDtZYXNoOwT5OCIjpwDDVh7B9lNJcpdFJoQBSEb6QdDcCZ6IqMIcrTVYP6Y1ngxwQ16BDhO+PY7VB+PlLotMBAOQjNLYA0RE9Fgs1EosGxaEYW1qQQjgvZ9jMeen0ygo1MldGhk5BiCZ6HRCfwuMY4CIiB6dUiHh/acD8UavBgCAryOuYszaY8jM4TR5ejAGIJlk5RWgeDFTToMnIno8kiRhfJe6CBvaAhZqBfacu4VnwyJw/W623KWRkWIAkknxTvBalQIWaqXM1RARVQ+9G3vgu1faooatFuduZmLAkoM4nnBX7rLICDEAyeTvbTDY+0NEVJmaeDngpwnt4e9hh9SsPDy//DC2nbghd1lkZBiAZMJtMIiIqo6ngyV+CG2Lbg1dkVegw6QN0fg8/Dx03EiV/sIAJBMGICKiqmWtVWH5iGCM6eAHAPhi1wW8si6Kg6MJAAOQbBiAiIiqnlIhYfZTAfj42SbQKBUIj72JgUsP4fKtLLlLI5kxAMnk733ANDJXQkRU/Q0O9sZ3oW3hbmeBiylZeHrJQfx59qbcZZGMGIBkwh4gIiLDaubtgG2vtkdLX0dk5hTgpa+PYfGfFyAExwWZIwYgmTAAEREZnqutBdaPaaNfOfrTHecxfv1xjgsyQwxAMkm/nwcAsLdUyVwJEZF50agU+L8BjTF/UGNolAr8djoZ/RcfROyNDLlLIwNiAJLJ3+sAcQwQEZEcnm9VC5teaQNPewvEp97DwKUHseloAm+JmQkGIJnwFhgRkfya13LEr5M6omuDGsgt0OHNzafw2vcnkJ1XIHdpVMUYgGRSHIC4DxgRkbwcrTVYObIl3ujVAAoJ2HI8EU8vPoiLKZlyl0ZViAFIJn9Pg2cAIiKSm0JRtJnqt2PbwNVWiwspWej3v4P4/tg13hKrphiAZFCoE8jMKepe5V5gRETGo01tZ/w6qSPa13XG/fxCvP7DSUzcEK3fwJqqDwYgGfxzuiV7gIiIjEsNWy3Wjm6N13s2gEoh4deTSej9xT4cuXxb7tKoEjEAyaB4/I+VRgm1kpeAiMjYKBUSJnStix/GtYOvsxVupOfg+RWH8ckfZ5FfqJO7PKoE/PaVAcf/EBGZhmbeDvh1UkcMDvaCEMCS3ZfwbNghxKfek7s0ekwMQDLgFHgiItNhrVXh42ebYskLLWBnocKJ6+no/cU+rDwQD52OA6RNFQOQDBiAiIhMT98mHvh9Sie0r+uMnHwd3v8lFiHLI3CFvUEmiQFIBgxARESmydPBEuteao0PBgbCWqPE0St30euLfVjF3iCTwwAkAwYgIiLTJUkShrb2KdEbNPev3qDLt7LkLo/KiQFIBn/vA8YARERkqrydrLDupdb4vwH/7A3ajy92XkBuQaHc5dF/YACSQTpngRERVQuSJGFYm6LeoI71XJBXoMPnO8+j9xf7EXGJ6wYZMwYgGfAWGBFR9eLtZIW1o1th0ZDmcLHR4vKtexiy4jBe++4E7tzLk7s8KgMDkAzS7hf9MthbaWSuhIiIKoskSejf1BO7XuuMoa1rQZKAzcev44nP9mD9kaso5CBpo8IAJIP0+0X7gLEHiIio+rG3VOODgY2xeVw7NHS3RVp2PmZtPY2n/neAt8WMCAOQDDJ4C4yIqNprUcsRP7/aAe/2C4CdhQpxSRkYsuIwxq2LwrU72XKXZ/YYgGSQlv3XLTAGICKiak2tVGBUez/seb0rhrfxgUICfjudjG4L9uLj388iI4e7zMuFAcjA8gt1uJdXND3SgQGIiMgsOFlr8P6AQGyf3BHt6jgjr0CHpXsuodPHu7Fi32Xk5HPavKExABlY8e0vALBjACIiMisN3e2wfkxrfDk8CHVqWCMtOx8fbI9D10/3YNPRBBRwp3mDYQAysOIp8LZaFZQKSeZqiIjI0CRJQs9G7vhjSid8/GwTeNpbICk9B29uPoUeC/fh5xM3OGPMABiADCztrwDE3h8iIvOmUiowONgbf07vgtl9/eFopcblW/fw6oZoPPn5XmyOus4eoSrEAGRg3AaDiIj+yUKtxJiOtbHvja6Y2r0+7C2LgtBr359A18/2YENkArfWqAIMQAbGKfBERFQWWws1Jnevh4MznsCbvRrC2VqDa3fuY+aWU+j88R6E7bmk30qJHh8DkIFxGwwiInoYG60K47rUwYE3n8A7TwXAzU6L5IwcfPT7WbSZtwtv/3gal7jr/GNjADKwNG6ESkRE5WCpUWJ0Bz/se6MrPnm2CRq62+J+fiG+OXwV3T7bi9FrjmL3uRQOmH5EKrkLMDf6HiCOASIionLQqpR4LtgbzwZ5IeLSbaw6GI9dZ1Pw51+Pmg6WeC7YC4ODveHpYCl3uSaDAcjAeAuMiIgehSRJaFfXBe3quiA+9R6+PnQFW6MTkZh2Hwt3XsCiXRfQuX4NDA72RteGrrBQK+Uu2agxABkYAxARET0uPxdrvNu/EWb0bojfTydjQ2QCjsTfwe5zt7D73C3YalXoGeiO/k090a6OM1RKjnj5NwYgA0vnGCAiIqokFmolBjSviQHNa+LyrSxsOnYN22JuICk9Bz9EXccPUdfhYqNB70AP9GjkhtZ+ztCoGIYABiCD068DZKmRuRIiIqpOatewwcze/nizZ0Mcu3oX204kYvupZKRm5eGbw1fxzeGrsNGq0Ll+DXQPcEXXBq5wsDLf7yIGIAPjLTAiIqpKCoWEVn5OaOXnhDn9GuHAxVTsOJOMnXEpuJWZi19PJeHXU0lQSEDjmvZoW8cF7eo4o6WvEyw15jNuiAHIwBiAiIjIUNRKBbo2KOrt+UAncDIxHTtjb2Jn3E2cTc7EievpOHE9Hcv2XoJaKaG5tyOa+zigubcDmnk7wt3eQu5TqDIMQAaUW1CI+/lFy5lzGjwRERmSQiGhmbcDmnk7YHrPBkhKv4+IS7dx6NJtHLqYihvpOYi8cgeRV+7oX+Nmp0VTLwc09LBDfTcb1HezhZ+LNdTVYFA1A5ABFff+SFLRbvBERERy8bC3xKAWXhjUwgtCCCTcycbhy7cRcy0dMdfScC45AzczcrEj9iZ2xN7Uv06tlODrbA0fZyt4OVqhpoMlvBwtUdPREi42WjhZa0xiCj6/hQ2oeB8wOws1FApJ5mqIiIiKSJIEH2dr+DhbI6Rl0bHsvAKcTszAyetpOH8zE+dvZuHCzUzcyyvEhZQsXEh58HYclmolHK3UcLDSwEqjhIVaCa1Kof9fSZLg62yFV7vVM9AZliZ7AFq6dCk++eQTJCUloVGjRli4cCE6duz4wPZ79+7FtGnTcObMGXh6euKNN95AaGhoiTabN2/G22+/jUuXLqFOnTr44IMPMHDgwKo+lf/E8T9ERGQqrDQq/WDqYkII3EjPwYWbmbh+9z4S0+4X/e/dbCSm3cede3nILxS4n1+I++mFuJGe88D3b1HLwXwD0KZNmzBlyhQsXboU7du3x5dffonevXsjNjYWtWrVKtU+Pj4effr0wdixY7Fu3TocPHgQ48ePR40aNfDMM88AACIiIhASEoL3338fAwcOxNatWzF48GAcOHAArVu3NvQpluDpYInZff2hNYGuQSIion+TJAk1HSxR8wFbbgghcC+vEHey8nAnOw93s/OQm1+InHwdcvILkVugQ25BIXSiaHyRnCQhhGy7qLVu3RotWrRAWFiY/pi/vz8GDBiAefPmlWr/5ptvYtu2bYiLi9MfCw0NxYkTJxAREQEACAkJQUZGBn777Td9m169esHR0REbNmwoV13Xr1+Ht7c3rl27Bi8vr0c9PSIiIjKginx/yzaMOy8vD1FRUejRo0eJ4z169MChQ4fKfE1ERESp9j179sSxY8eQn5//0DYPek8AyM3NRUZGhv6RmZn5KKdEREREJkK2AJSamorCwkK4ubmVOO7m5obk5OQyX5OcnFxm+4KCAqSmpj60zYPeEwDmzZsHe3t7/SMgIOBRTomIiIhMhOwT+SWp5GwoIUSpY//V/t/HK/qeM2fORHp6uv4RGxtb7vqJiIjI9Mg2CNrFxQVKpbJUz0xKSkqpHpxi7u7uZbZXqVRwdnZ+aJsHvScAaLVaaLV/D8bKyMio0LkQERGRaZGtB0ij0SAoKAjh4eEljoeHh6Ndu3ZlvqZt27al2u/YsQPBwcFQq9UPbfOg9yQiIiLzI+s0+GnTpmH48OEIDg5G27ZtsXz5ciQkJOjX9Zk5cyYSExOxdu1aAEUzvhYvXoxp06Zh7NixiIiIwMqVK0vM7po8eTI6deqEjz76CE8//TR++ukn7Ny5EwcOHJDlHImIiMj4yBqAQkJCcPv2bcydOxdJSUkIDAzE9u3b4ePjAwBISkpCQkKCvr2fnx+2b9+OqVOnYsmSJfD09MSiRYv0awABQLt27bBx40bMnj0bb7/9NurUqYNNmzbJvgYQERERGQ9Z1wEyVlwHiIiIyPSYxDpARERERHJhACIiIiKzwwBEREREZocBiIiIiMwOAxARERGZHVmnwRur4olxmZmZXBWaiIjIRBRvZl6eCe4MQGXIysoCAG6KSkREZIKKv8cfhusAlaGwsBDnz5+Hh4cHFIrKvUuYkZGhX6PAzs6uUt+bKo7Xw7jwehgXXg/jw2vycDqdDklJSahfvz6USuVD27IHqAxKpRL+/v5V+hl2dnb8y2tEeD2MC6+HceH1MD68Jg/m4OBQrnYcBE1ERERmhwGIiIiIzA4DkIFptVrMmTMHWq1W7lIIvB7GhtfDuPB6GB9ek8rDQdBERERkdtgDRERERGaHAYiIiIjMDgMQERERmR0GICIiIjI7DEAGtHTpUvj5+cHCwgJBQUHYv3+/3CWZnHnz5qFly5awtbWFq6srBgwYgHPnzpVoI4TAu+++C09PT1haWqJLly44c+ZMiTa5ubl49dVX4eLiAmtra/Tv3x/Xr18v0ebu3bsYPnw47O3tYW9vj+HDhyMtLa1Em4SEBPTr1w/W1tZwcXHBpEmTkJeXVyXnbgrmzZsHSZIwZcoU/TFeD8NKTEzEsGHD4OzsDCsrKzRr1gxRUVH653k9DKegoACzZ8+Gn58fLC0tUbt2bcydOxc6nU7fhtdDRoIMYuPGjUKtVosVK1aI2NhYMXnyZGFtbS2uXr0qd2kmpWfPnmL16tXi9OnTIiYmRvTt21fUqlVLZGVl6dvMnz9f2Nrais2bN4tTp06JkJAQ4eHhITIyMvRtQkNDRc2aNUV4eLg4fvy46Nq1q2jatKkoKCjQt+nVq5cIDAwUhw4dEocOHRKBgYHiqaee0j9fUFAgAgMDRdeuXcXx48dFeHi48PT0FBMnTjTMD8PIREZGCl9fX9GkSRMxefJk/XFeD8O5c+eO8PHxEaNGjRJHjhwR8fHxYufOneLixYv6NrwehvN///d/wtnZWfzyyy8iPj5efP/998LGxkYsXLhQ34bXQz4MQAbSqlUrERoaWuJYw4YNxYwZM2SqqHpISUkRAMTevXuFEELodDrh7u4u5s+fr2+Tk5Mj7O3txbJly4QQQqSlpQm1Wi02btyob5OYmCgUCoX4/fffhRBCxMbGCgDi8OHD+jYRERECgDh79qwQQojt27cLhUIhEhMT9W02bNggtFqtSE9Pr7qTNkKZmZmiXr16Ijw8XHTu3FkfgHg9DOvNN98UHTp0eODzvB6G1bdvXzF69OgSxwYNGiSGDRsmhOD1kBtvgRlAXl4eoqKi0KNHjxLHe/TogUOHDslUVfWQnp4OAHBycgIAxMfHIzk5ucTPWqvVonPnzvqfdVRUFPLz80u08fT0RGBgoL5NREQE7O3t0bp1a32bNm3awN7evkSbwMBAeHp66tv07NkTubm5JW45mIMJEyagb9++6N69e4njvB6GtW3bNgQHB+O5556Dq6srmjdvjhUrVuif5/UwrA4dOmDXrl04f/48AODEiRM4cOAA+vTpA4DXQ27cDNUAUlNTUVhYCDc3txLH3dzckJycLFNVpk8IgWnTpqFDhw4IDAwEAP3Ps6yf9dWrV/VtNBoNHB0dS7Upfn1ycjJcXV1Lfaarq2uJNv/+HEdHR2g0GrO6rhs3bsTx48dx9OjRUs/xehjW5cuXERYWhmnTpuGtt95CZGQkJk2aBK1WixEjRvB6GNibb76J9PR0NGzYEEqlEoWFhfjggw8wZMgQAPz9kBsDkAFJklTiz0KIUseo/CZOnIiTJ0/iwIEDpZ57lJ/1v9uU1f5R2lRn165dw+TJk7Fjxw5YWFg8sB2vh2HodDoEBwfjww8/BAA0b94cZ86cQVhYGEaMGKFvx+thGJs2bcK6devw7bffolGjRoiJicGUKVPg6emJkSNH6tvxesiDt8AMwMXFBUqlslTKTklJKZXIqXxeffVVbNu2Dbt374aXl5f+uLu7OwA89Gft7u6OvLw83L1796Ftbt68Wepzb926VaLNvz/n7t27yM/PN5vrGhUVhZSUFAQFBUGlUkGlUmHv3r1YtGgRVCqV/ufA62EYHh4eCAgIKHHM398fCQkJAPj7YWivv/46ZsyYgeeffx6NGzfG8OHDMXXqVMybNw8Ar4fcGIAMQKPRICgoCOHh4SWOh4eHo127djJVZZqEEJg4cSK2bNmCP//8E35+fiWe9/Pzg7u7e4mfdV5eHvbu3av/WQcFBUGtVpdok5SUhNOnT+vbtG3bFunp6YiMjNS3OXLkCNLT00u0OX36NJKSkvRtduzYAa1Wi6CgoMo/eSPUrVs3nDp1CjExMfpHcHAwhg4dipiYGNSuXZvXw4Dat29falmI8+fPw8fHBwB/PwwtOzsbCkXJr1mlUqmfBs/rITMDD7o2W8XT4FeuXCliY2PFlClThLW1tbhy5YrcpZmUcePGCXt7e7Fnzx6RlJSkf2RnZ+vbzJ8/X9jb24stW7aIU6dOiSFDhpQ5rdTLy0vs3LlTHD9+XDzxxBNlTitt0qSJiIiIEBEREaJx48ZlTivt1q2bOH78uNi5c6fw8vIy62mlQogSs8CE4PUwpMjISKFSqcQHH3wgLly4INavXy+srKzEunXr9G14PQxn5MiRombNmvpp8Fu2bBEuLi7ijTfe0Lfh9ZAPA5ABLVmyRPj4+AiNRiNatGihn7pN5QegzMfq1av1bXQ6nZgzZ45wd3cXWq1WdOrUSZw6darE+9y/f19MnDhRODk5CUtLS/HUU0+JhISEEm1u374thg4dKmxtbYWtra0YOnSouHv3bok2V69eFX379hWWlpbCyclJTJw4UeTk5FTV6ZuEfwcgXg/D+vnnn0VgYKDQarWiYcOGYvny5SWe5/UwnIyMDDF58mRRq1YtYWFhIWrXri1mzZolcnNz9W14PeQjCSGEnD1QRERERIbGMUBERERkdhiAiIiIyOwwABEREZHZYQAiIiIis8MARERERGaHAYiIiIjMDgMQERERmR0GICIiIjI7DEBERH/x9fXFwoUL5S6DiAyAAYiIZDFq1CgMGDAAANClSxdMmTLFYJ+9Zs0aODg4lDp+9OhRvPzyywarg4jko5K7ACKiypKXlweNRvPIr69Ro0YlVkNExow9QEQkq1GjRmHv3r344osvIEkSJEnClStXAACxsbHo06cPbGxs4ObmhuHDhyM1NVX/2i5dumDixImYNm0aXFxc8OSTTwIAFixYgMaNG8Pa2hre3t4YP348srKyAAB79uzBiy++iPT0dP3nvfvuuwBK3wJLSEjA008/DRsbG9jZ2WHw4MG4efOm/vl3330XzZo1wzfffANfX1/Y29vj+eefR2Zmpr7NDz/8gMaNG8PS0hLOzs7o3r077t27V0U/TSIqLwYgIpLVF198gbZt22Ls2LFISkpCUlISvL29kZSUhM6dO6NZs2Y4duwYfv/9d9y8eRODBw8u8fqvv/4aKpUKBw8exJdffgkAUCgUWLRoEU6fPo2vv/4af/75J9544w0AQLt27bBw4ULY2dnpP2/69Oml6hJCYMCAAbhz5w727t2L8PBwXLp0CSEhISXaXbp0CT/++CN++eUX/PLLL9i7dy/mz58PAEhKSsKQIUMwevRoxMXFYc+ePRg0aBC4BzWR/HgLjIhkZW9vD41GAysrK7i7u+uPh4WFoUWLFvjwww/1x1atWgVvb2+cP38e9evXBwDUrVsXH3/8cYn3/Od4Ij8/P7z//vsYN24cli5dCo1GA3t7e0iSVOLz/m3nzp04efIk4uPj4e3tDQD45ptv0KhRIxw9ehQtW7YEAOh0OqxZswa2trYAgOHDh2PXrl344IMPkJSUhIKCAgwaNAg+Pj4AgMaNGz/GT4uIKgt7gIjIKEVFRWH37t2wsbHRPxo2bAigqNelWHBwcKnX7t69G08++SRq1qwJW1tbjBgxArdv367Qrae4uDh4e3vrww8ABAQEwMHBAXFxcfpjvr6++vADAB4eHkhJSQEANG3aFN26dUPjxo3x3HPPYcWKFbh79275fwhEVGUYgIjIKOl0OvTr1w8xMTElHhcuXECnTp307aytrUu87urVq+jTpw8CAwOxefNmREVFYcmSJQCA/Pz8cn++EAKSJP3ncbVaXeJ5SZKg0+kAAEqlEuHh4fjtt98QEBCA//3vf2jQoAHi4+PLXQcRVQ0GICKSnUajQWFhYYljLVq0wJkzZ+Dr64u6deuWePw79PzTsWPHUFBQgM8++wxt2rRB/fr1cePGjf/8vH8LCAhAQkICrl27pj8WGxuL9PR0+Pv7l/vcJElC+/bt8d577yE6OhoajQZbt24t9+uJqGowABGR7Hx9fXHkyBFcuXIFqamp0Ol0mDBhAu7cuYMhQ4YgMjISly9fxo4dOzB69OiHhpc6deqgoKAA//vf/3D58mV88803WLZsWanPy8rKwq5du5Camors7OxS79O9e3c0adIEQ4cOxfHjxxEZGYkRI0agc+fOZd52K8uRI0fw4Ycf4tixY0hISMCWLVtw69atCgUoIqoaDEBEJLvp06dDqVQiICAANWrUQEJCAjw9PXHw4EEUFhaiZ8+eCAwMxOTJk2Fvbw+F4sH/19WsWTMsWLAAH330EQIDA7F+/XrMmzevRJt27dohNDQUISEhqFGjRqlB1EBRz82PP/4IR0dHdOrUCd27d0ft2rWxadOmcp+XnZ0d9u3bhz59+qB+/fqYPXs2PvvsM/Tu3bv8PxwiqhKS4HxMIiIiMjPsASIiIiKzwwBEREREZocBiIiIiMwOAxARERGZHQYgIiIiMjsMQERERGR2GICIiIjI7DAAERERkdlhACIiIiKzwwBEREREZocBiIiIiMwOAxARERGZHQYgIiIiMjsMQERERGR2GICIiIjI7DAAERERkdlhACIiIiKzwwBEREREZocBiIiIiMwOAxARERGZHZXcBRgjIQQyMzPlLoOIiIgega2tLSRJemgbBqAyZGZmwt7eXu4yiIiI6BGkp6fDzs7uoW0kIYQwUD0moyp7gDIyMuDt7Y1r167958UxFqzZMFizYbBmwzDFmgHTrJs1l8YeoEckSVKV/yWys7Mzmb+oxVizYbBmw2DNhmGKNQOmWTdrrhgOgiYiIiKzwwBEREREZocByMC0Wi3mzJkDrVYrdynlxpoNgzUbBms2DFOsGTDNulnzo+EgaCIiIjI77AEiIiIis8MARERERGaHAYiIiIjMDgMQERERmR0GIANaunQp/Pz8YGFhgaCgIOzfv1/ukvTmzZuHli1bwtbWFq6urhgwYADOnTtXoo0QAu+++y48PT1haWmJLl264MyZMzJVXNq8efMgSRKmTJmiP2aMNScmJmLYsGFwdnaGlZUVmjVrhqioKP3zxlZzQUEBZs+eDT8/P1haWqJ27dqYO3cudDqdUdW8b98+9OvXD56enpAkCT/++GOJ58tTY25uLl599VW4uLjA2toa/fv3x/Xr12WpOT8/H2+++SYaN24Ma2treHp6YsSIEbhx44bR1vxvr7zyCiRJwsKFC42+5ri4OPTv3x/29vawtbVFmzZtkJCQYLQ1Z2VlYeLEifDy8oKlpSX8/f0RFhZWoo0ha66s7xCD/pwFGcTGjRuFWq0WK1asELGxsWLy5MnC2tpaXL16Ve7ShBBC9OzZU6xevVqcPn1axMTEiL59+4patWqJrKwsfZv58+cLW1tbsXnzZnHq1CkREhIiPDw8REZGhoyVF4mMjBS+vr6iSZMmYvLkyfrjxlbznTt3hI+Pjxg1apQ4cuSIiI+PFzt37hQXL1402pr/7//+Tzg7O4tffvlFxMfHi++//17Y2NiIhQsXGlXN27dvF7NmzRKbN28WAMTWrVtLPF+eGkNDQ0XNmjVFeHi4OH78uOjatato2rSpKCgoMHjNaWlponv37mLTpk3i7NmzIiIiQrRu3VoEBQWVeA9jqvmftm7dKpo2bSo8PT3F559/btQ1X7x4UTg5OYnXX39dHD9+XFy6dEn88ssv4ubNm0Zb85gxY0SdOnXE7t27RXx8vPjyyy+FUqkUP/74oyw1V9Z3iCFrZgAykFatWonQ0NASxxo2bChmzJghU0UPl5KSIgCIvXv3CiGE0Ol0wt3dXcyfP1/fJicnR9jb24tly5bJVaYQQojMzExRr149ER4eLjp37qwPQMZY85tvvik6dOjwwOeNsea+ffuK0aNHlzg2aNAgMWzYMCGEcdb87y+M8tSYlpYm1Gq12Lhxo75NYmKiUCgU4vfffzd4zWWJjIwUAPT/cDLWmq9fvy5q1qwpTp8+LXx8fEoEIGOsOSQkRP/3uSzGWHOjRo3E3LlzSxxr0aKFmD17tlHU/CjfIYaumbfADCAvLw9RUVHo0aNHieM9evTAoUOHZKrq4dLT0wEATk5OAID4+HgkJyeXOAetVovOnTvLfg4TJkxA37590b179xLHjbHmbdu2ITg4GM899xxcXV3RvHlzrFixQv+8MdbcoUMH7Nq1C+fPnwcAnDhxAgcOHECfPn2MtuZ/K0+NUVFRyM/PL9HG09MTgYGBRnMe6enpkCQJDg4OAIyzZp1Oh+HDh+P1119Ho0aNSj1vbDXrdDr8+uuvqF+/Pnr27AlXV1e0bt26xC0nY6sZKPq93LZtGxITEyGEwO7du3H+/Hn07NnTKGp+lO8QQ9fMAGQAqampKCwshJubW4njbm5uSE5OlqmqBxNCYNq0aejQoQMCAwMBQF+nsZ3Dxo0bcfz4ccybN6/Uc8ZY8+XLlxEWFoZ69erhjz/+QGhoKCZNmoS1a9cCMM6a33zzTQwZMgQNGzaEWq1G8+bNMWXKFAwZMgSAcdb8b+WpMTk5GRqNBo6Ojg9sI6ecnBzMmDEDL7zwgn7zSGOs+aOPPoJKpcKkSZPKfN7Yak5JSUFWVhbmz5+PXr16YceOHRg4cCAGDRqEvXv3GmXNALBo0SIEBATAy8sLGo0GvXr1wtKlS9GhQwcA8tb8qN8hhq6Zu8EbkCRJJf4shCh1zBhMnDgRJ0+exIEDB0o9Z0zncO3aNUyePBk7duyAhYXFA9sZU806nQ7BwcH48MMPAQDNmzfHmTNnEBYWhhEjRujbGVPNmzZtwrp16/Dtt9+iUaNGiImJwZQpU+Dp6YmRI0fq2xlTzQ/yKDUaw3nk5+fj+eefh06nw9KlS/+zvVw1R0VF4YsvvsDx48cr/Ply1Vw8mP/pp5/G1KlTAQDNmjXDoUOHsGzZMnTu3PmBr5Xz78aiRYtw+PBhbNu2DT4+Pti3bx/Gjx8PDw+PUr3h/2SImiv7O6SqamYPkAG4uLhAqVSWSrApKSml0rDcXn31VWzbtg27d++Gl5eX/ri7uzsAGNU5REVFISUlBUFBQVCpVFCpVNi7dy8WLVoElUqlr8uYavbw8EBAQECJY/7+/vrZJsb4c3799dcxY8YMPP/882jcuDGGDx+OqVOn6nvdjLHmfytPje7u7sjLy8Pdu3cf2EYO+fn5GDx4MOLj4xEeHq7v/QGMr+b9+/cjJSUFtWrV0v9OXr16Fa+99hp8fX2NsmYXFxeoVKr//L00pprv37+Pt956CwsWLEC/fv3QpEkTTJw4ESEhIfj0009lrflxvkMMXTMDkAFoNBoEBQUhPDy8xPHw8HC0a9dOpqpKEkJg4sSJ2LJlC/7880/4+fmVeN7Pzw/u7u4lziEvLw979+6V7Ry6deuGU6dOISYmRv8IDg7G0KFDERMTg9q1axtdze3bty81NfT8+fPw8fEBYJw/5+zsbCgUJf+vQqlU6v/lbIw1/1t5agwKCoJarS7RJikpCadPn5btPIrDz4ULF7Bz5044OzuXeN7Yah4+fDhOnjxZ4nfS09MTr7/+Ov744w+jrFmj0aBly5YP/b00tprz8/ORn5//0N9LQ9dcGd8hBv85V/qwaipT8TT4lStXitjYWDFlyhRhbW0trly5IndpQgghxo0bJ+zt7cWePXtEUlKS/pGdna1vM3/+fGFvby+2bNkiTp06JYYMGWI00+CL/XMWmBDGV3NkZKRQqVTigw8+EBcuXBDr168XVlZWYt26dUZb88iRI0XNmjX10+C3bNkiXFxcxBtvvGFUNWdmZoro6GgRHR0tAIgFCxaI6Oho/Yyp8tQYGhoqvLy8xM6dO8Xx48fFE088UaVTnR9Wc35+vujfv7/w8vISMTExJX4vc3NzjbLmsvx7Fpgx1rxlyxahVqvF8uXLxYULF8T//vc/oVQqxf79+4225s6dO4tGjRqJ3bt3i8uXL4vVq1cLCwsLsXTpUllqrqzvEEPWzABkQEuWLBE+Pj5Co9GIFi1a6KcHGgMAZT5Wr16tb6PT6cScOXOEu7u70Gq1olOnTuLUqVPyFV2GfwcgY6z5559/FoGBgUKr1YqGDRuK5cuXl3je2GrOyMgQkydPFrVq1RIWFhaidu3aYtasWSW+hI2h5t27d5f5d3jkyJHlrvH+/fti4sSJwsnJSVhaWoqnnnpKJCQkyFJzfHz8A38vd+/ebZQ1l6WsAGSMNa9cuVLUrVtXWFhYiKZNm5ZYT8cYa05KShKjRo0Snp6ewsLCQjRo0EB89tlnQqfTyVJzZX2HGLJm6a/CiYiIiMwGxwARERGR2WEAIiIiIrPDAERERERmhwGIiIiIzA4DEBEREZkdBiAiIiIyOwxAREREZHYYgIiIHkCSJPz4449yl0FEVYABiIiM0qhRoyBJUqlHr1695C6NiKoBldwFEBE9SK9evbB69eoSx7RarUzVEFF1wh4gIjJaWq0W7u7uJR6Ojo4Aim5PhYWFoXfv3rC0tISfnx++//77Eq8/deoUnnjiCVhaWsLZ2Rkvv/wysrKySrRZtWoVGjVqBK1WCw8PD0ycOLHE86mpqRg4cCCsrKxQr149bNu2Tf/c3bt3MXToUNSoUQOWlpaoV69eqcBGRMaJAYiITNbbb7+NZ555BidOnMCwYcMwZMgQxMXFAQCys7PRq1cvODo64ujRo/j++++xc+fOEgEnLCwMEyZMwMsvv4xTp05h27ZtqFu3bonPeO+99zB48GCcPHkSffr0wdChQ3Hnzh3958fGxuK3335DXFwcwsLC4OLiYrgfABE9uirZYpWI6DGNHDlSKJVKYW1tXeIxd+5cIUTR7tOhoaElXtO6dWsxbtw4IYQQy5cvF46OjiIrK0v//K+//ioUCoVITk4WQgjh6ekpZs2a9cAaAIjZs2fr/5yVlSUkSRK//fabEEKIfv36iRdffLFyTpiIDIpjgIjIaHXt2hVhYWEljjk5Oen/u23btiWea9u2LWJiYgAAcXFxaNq0KaytrfXPt2/fHjqdDufOnYMkSbhx4wa6dev20BqaNGmi/29ra2vY2toiJSUFADBu3Dg888wzOH78OHr06IEBAwagXbt2j3SuRGRYDEBEZLSsra1L3ZL6L5IkAQCEEPr/LquNpaVlud5PrVaXeq1OpwMA9O7dG1evXsWvv/6KnTt3olu3bpgwYQI+/fTTCtVMRIbHMUBEZLIOHz5c6s8NGzYEAAQEBCAmJgb37t3TP3/w4EEoFArUr18ftra28PX1xa5dux6rhho1amDUqFFYt24dFi5ciOXLlz/W+xGRYbAHiIiMVm5uLpKTk0scU6lU+oHG33//PYKDg9GhQwesX78ekZGRWLlyJQBg6NChmDNnDkaOHIl3330Xt27dwquvvorhw4fDzc0NAPDuu+8iNDQUrq6u6N27NzIzM3Hw4EG8+uqr5arvnXfeQVBQEBo1aoTc3Fz88ssv8Pf3r8SfABFVFQYgIjJav//+Ozw8PEoca9CgAc6ePQugaIbWxo0bMX78eLi7u2P9+vUICAgAAFhZWeGPP/7A5MmT0bJlS1hZWeGZZ57BggUL9O81cuRI5OTk4PPPP8f06dPh4uKCZ599ttz1aTQazJw5E1euXIGlpSU6duyIjRs3VsKZE1FVk4QQQu4iiIgqSpIkbN26FQMGDJC7FCIyQRwDRERERGaHAYiIiIjMDscAEZFJ4t17Inoc7AEiIiIis8MARERERGaHAYiIiIjMDgMQERERmR0GICIiIjI7DEBERERkdhiAiIiIyOwwABEREZHZYQAiIiIis/P/U/LODa1P4nYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "model = torch.nn.Linear(1, 1)\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n",
    "\n",
    "\n",
    "dm = CustomDataModule()\n",
    "dm.setup(\"training\")\n",
    "num_steps = num_epochs * len(dm.train_dataloader())\n",
    "scheduler = CosineAnnealingWarmupRestarts(optimizer, warmup_steps=num_steps * 0.05, first_cycle_steps=num_steps)\n",
    "lrs = []\n",
    "\n",
    "\n",
    "for i in range(num_steps):\n",
    "    optimizer.step()\n",
    "    lrs.append(optimizer.param_groups[0][\"lr\"])\n",
    "    scheduler.step()\n",
    "\n",
    "\n",
    "ax1 = plt.subplot(1, 1, 1)\n",
    "ax1.set_ylabel(\"Learning rate\")\n",
    "ax1.set_xlabel(\"Iterations\")\n",
    "ax1.plot(lrs)\n",
    "\n",
    "###################\n",
    "# Set second x-axis\n",
    "ax2 = ax1.twiny()\n",
    "newlabel = list(range(num_epochs+1))\n",
    "\n",
    "newpos = [e * (num_steps // num_epochs) for e in newlabel]\n",
    "\n",
    "ax2.set_xticks(newpos[::20])\n",
    "ax2.set_xticklabels(newlabel[::20])\n",
    "\n",
    "ax2.xaxis.set_ticks_position('bottom')\n",
    "ax2.xaxis.set_label_position('bottom')\n",
    "ax2.spines['bottom'].set_position(('outward', 45))\n",
    "ax2.set_xlabel('Epochs')\n",
    "ax2.set_xlim(ax1.get_xlim());\n",
    "\n",
    "plt.savefig('6-cosine-batch-decay-warmstart_lr.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ce808943-a20e-44d2-9303-c897226b37df",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PyTorchMLP(torch.nn.Module):\n",
    "    def __init__(self, num_features, num_classes):\n",
    "        super().__init__()\n",
    "\n",
    "        self.all_layers = torch.nn.Sequential(\n",
    "            # 1st hidden layer\n",
    "            torch.nn.Linear(num_features, 100),\n",
    "            torch.nn.BatchNorm1d(100),\n",
    "            torch.nn.ReLU(),\n",
    "            \n",
    "            # 2nd hidden layer\n",
    "            torch.nn.Linear(100, 50),\n",
    "            torch.nn.BatchNorm1d(50),\n",
    "            torch.nn.ReLU(),\n",
    "            \n",
    "            # output layer\n",
    "            torch.nn.Linear(50, num_classes),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.flatten(x, start_dim=1)\n",
    "        logits = self.all_layers(x)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "59a6a00d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class LightningModel(L.LightningModule):\n",
    "    def __init__(self, model, learning_rate, cosine_t_max):\n",
    "        super().__init__()\n",
    "\n",
    "        self.learning_rate = learning_rate\n",
    "        self.cosine_t_max = cosine_t_max\n",
    "        self.model = model\n",
    "\n",
    "        self.save_hyperparameters(ignore=[\"model\"])\n",
    "\n",
    "        self.train_acc = torchmetrics.Accuracy()\n",
    "        self.val_acc = torchmetrics.Accuracy()\n",
    "        self.test_acc = torchmetrics.Accuracy()\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    def _shared_step(self, batch):\n",
    "        features, true_labels = batch\n",
    "        logits = self(features)\n",
    "\n",
    "        loss = F.cross_entropy(logits, true_labels)\n",
    "        predicted_labels = torch.argmax(logits, dim=1)\n",
    "        return loss, true_labels, predicted_labels\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "\n",
    "        self.log(\"train_loss\", loss)\n",
    "        self.train_acc(predicted_labels, true_labels)\n",
    "        self.log(\n",
    "            \"train_acc\", self.train_acc, prog_bar=True, on_epoch=True, on_step=False\n",
    "        )\n",
    "        return loss\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "\n",
    "        self.log(\"val_loss\", loss, prog_bar=True)\n",
    "        self.val_acc(predicted_labels, true_labels)\n",
    "        self.log(\"val_acc\", self.val_acc, prog_bar=True)\n",
    "\n",
    "    def test_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.test_acc(predicted_labels, true_labels)\n",
    "        self.log(\"test_acc\", self.test_acc)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        opt = torch.optim.SGD(self.parameters(), lr=self.learning_rate)\n",
    "        sch = CosineAnnealingWarmupRestarts(opt, warmup_steps=num_steps * 0.05, first_cycle_steps=num_steps)\n",
    "\n",
    "\n",
    "        return {\n",
    "            \"optimizer\": opt,\n",
    "            \"lr_scheduler\": {\n",
    "                \"scheduler\": sch,\n",
    "                \"monitor\": \"train_loss\",\n",
    "                \"interval\": \"step\", # step means \"batch\" here, default: epoch\n",
    "                \"frequency\": 1, # default\n",
    "            },\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "094b0c74-cf04-4395-83c1-22fbe94cb9c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightning.pytorch.callbacks import ModelCheckpoint\n",
    "\n",
    "callbacks = [\n",
    "    ModelCheckpoint(save_top_k=1, mode=\"max\", monitor=\"val_acc\", save_last=True)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e3e70623",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 123\n",
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "03009f116c004650bcdbd3128c36e951",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Finding best initial lr:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_steps=100` reached.\n",
      "Learning rate set to 0.15848931924611143\n",
      "Restoring states from the checkpoint path at /Users/sebastianraschka/Desktop/cosine/.lr_find_45c895f1-9e0b-4909-86f2-aeb8e091a6ab.ckpt\n"
     ]
    }
   ],
   "source": [
    "%%capture --no-display\n",
    "\n",
    "L.seed_everything(123)\n",
    "dm = CustomDataModule()\n",
    "\n",
    "pytorch_model = PyTorchMLP(num_features=100, num_classes=2)\n",
    "lightning_model = LightningModel(model=pytorch_model, learning_rate=0.1, cosine_t_max=num_steps)\n",
    "\n",
    "trainer = L.Trainer(\n",
    "    max_epochs=num_epochs,\n",
    "    auto_lr_find=True,\n",
    "    accelerator=\"cpu\",\n",
    "    devices=\"auto\",\n",
    "    logger=CSVLogger(save_dir=\"logs/\", name=\"my-model\"),\n",
    "    deterministic=True,\n",
    "    callbacks=callbacks\n",
    ")\n",
    "\n",
    "results = trainer.tune(model=lightning_model, datamodule=dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0542570a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/cb/1y0v6fzx5c54sztp5nqq39jc0000gn/T/ipykernel_17228/735961897.py:3: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = results[\"lr_find\"].plot(suggest=True)\n",
    "# fig.savefig(\"lr_suggest.pdf\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e9293189",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.15848931924611143\n"
     ]
    }
   ],
   "source": [
    "# get suggestion\n",
    "new_lr = results[\"lr_find\"].suggestion()\n",
    "print(new_lr)\n",
    "\n",
    "# update hparams of the model\n",
    "lightning_model.hparams.learning_rate = new_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eb1581b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  | Name      | Type       | Params\n",
      "-----------------------------------------\n",
      "0 | model     | PyTorchMLP | 15.6 K\n",
      "1 | train_acc | Accuracy   | 0     \n",
      "2 | val_acc   | Accuracy   | 0     \n",
      "3 | test_acc  | Accuracy   | 0     \n",
      "-----------------------------------------\n",
      "15.6 K    Trainable params\n",
      "0         Non-trainable params\n",
      "15.6 K    Total params\n",
      "0.062     Total estimated model params size (MB)\n"
     ]
    },
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       "version_major": 2,
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      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
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       "model_id": "3a500ce76ea74d71910344125e410999",
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      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
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    },
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     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f16a2ff09bce455cb56ab1f2603a87ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2201acdda1f4fcbaf5568e593262b01",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d1d4066d68b046738d8a1cfa89c6f33c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=200` reached.\n"
     ]
    }
   ],
   "source": [
    "trainer.fit(model=lightning_model, datamodule=dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4c6fa764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "metrics = pd.read_csv(f\"{trainer.logger.log_dir}/metrics.csv\")\n",
    "\n",
    "aggreg_metrics = []\n",
    "agg_col = \"epoch\"\n",
    "for i, dfg in metrics.groupby(agg_col):\n",
    "    agg = dict(dfg.mean())\n",
    "    agg[agg_col] = i\n",
    "    aggreg_metrics.append(agg)\n",
    "\n",
    "df_metrics = pd.DataFrame(aggreg_metrics)\n",
    "df_metrics[[\"train_loss\", \"val_loss\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"Loss\"\n",
    ")\n",
    "\n",
    "\n",
    "df_metrics[[\"train_acc\", \"val_acc\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"ACC\"\n",
    ")\n",
    "\n",
    "plt.ylim([0.7, 1.0])\n",
    "\n",
    "plt.savefig('6-cosine-batch-decay-warmstart_acc.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0df61ef8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Restoring states from the checkpoint path at logs/my-model/version_19/checkpoints/epoch=112-step=50850.ckpt\n",
      "Loaded model weights from checkpoint at logs/my-model/version_19/checkpoints/epoch=112-step=50850.ckpt\n",
      "/Users/sebastianraschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:231: PossibleUserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bea392cf5a274779874200f72e90a4aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.890999972820282     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.890999972820282    \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "[{'test_acc': 0.890999972820282}]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model=lightning_model, datamodule=dm, ckpt_path=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1c17a3c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Restoring states from the checkpoint path at logs/my-model/version_19/checkpoints/last.ckpt\n",
      "Loaded model weights from checkpoint at logs/my-model/version_19/checkpoints/last.ckpt\n"
     ]
    },
    {
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       "model_id": "ec88d2f8901d4360b35e32fcfe937094",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
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     "output_type": "display_data"
    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">     0.890749990940094     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m    0.890749990940094    \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{'test_acc': 0.890749990940094}]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model=lightning_model, datamodule=dm, ckpt_path=\"last\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "74b9836a-a318-4ef3-a2c5-693268269ca3",
   "metadata": {},
   "outputs": [],
   "source": []
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